Due to the free-form nature of data, it can be noisy and requires cleansing before further processing. To do so, we processed texts in our dataset (both pre and post-COVID-19) to remove sources of noise including stopwords and generic terms using the popular Natural Language Toolkit (NLTK) [1]}. Due to the nature of free-form texts, we expected some un-intelligible words in our dataset like `gjhgdjfg', `jshdfkj', etc. However, we did not encounter any such words. Typos were corrected using publicly available spellchecker software.
